{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-30T17:41:44.105867Z",
     "start_time": "2021-04-30T17:41:43.917323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.000000e+00  9.948020e-01  9.792620e-01 ... -3.194259e-06 -3.162205e-06\n",
      " -3.097697e-06]\n",
      "[ 1.00000000e+00  9.94982066e-01  9.79969488e-01 ... -1.01569200e-03\n",
      " -9.87630277e-04 -9.49814351e-04]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dhem/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff37555b350>]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 386
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = fastread_np('../1d-corr-t.dat_0.1_0_40_1')\n",
    "data2 = fastread_np('./prop-pol.dat')\n",
    "plt.plot(data1[:,0], data1[:,1])\n",
    "plt.plot(data2[:,0], data2[:,1])\n",
    "print(data1[:,1])\n",
    "print(data2[:,1])\n",
    "plt.plot(np.fft.ifft(data1[:,1] +1.j*data1[:,2]))\n",
    "# plt.xlim(0, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
